Abstract

The privacy and the confidentiality limit the amount of the accessible information in investigating socioeconomic systems. Reconstructing network with limited information is of great application importance. Most existing network reconstruction methods concentrate on completely connected networks, undirected or unweighed networks. In the paper, we present a novel approach to reconstruct the topological structure of directed weighted network (DWN) for estimating multilateral trade behaviors on the World Trade Web using limited information: the values of out- and in-strengths of nodes, and the total number of links and nodes. Our approach uses first out- and in-strength fitnesses to estimate the unknown out- and in-degrees. Then we propose a directed enhanced configuration model (DECM) and reconstruct the topological structure of real DWN. This approach is experimentally verified on real-world networks, using the significantly topological properties: assortativity and clustering. The results show a good agreement between these quantities calculated on real DWN and its DECM-induced ensemble averages. Furthermore, we compare DECM algorithm with weighted node similarity algorithms for link prediction, demonstrating that DECM outperforms similarity-based link prediction algorithms. Finally, the comparison between DECM and directed weighted configuration model (DWCM) using Information-theoretic criteria rigorously confirms that DECM evidently outperforms DWCM for reconstructing network topological structure. Accordingly, this approach can be employed as an important tool for providing deeper insights into the privacy protection of socioeconomic networks.